Roadheader performance prediction using genetic programming (GP) and gene expression programming (GEP) techniques

Roadheading machines play a vital role in excavation operation in tunneling and mining industries notably when selective mining is required. Roadheaders are more effective in soft to medium rock formations due to a higher cutting rate in such strata. A precise prediction of machine’s performance is...

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Bibliographic Details
Published in:Environmental earth sciences Vol. 76; no. 16; pp. 1 - 12
Main Authors: Shirani Faradonbeh, Roohollah, Salimi, Alireza, Monjezi, Masoud, Ebrahimabadi, Arash, Moormann, Christian
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2017
Springer Nature B.V
Subjects:
Biogeosciences
coal
Coal mines
Coal mining
compression strength
Compressive strength
Cutting
Earth and Environmental Science
Earth Sciences
Environmental Science and Engineering
Excavation
Gene expression
Genetic algorithms
Geochemistry
Geology
Geomechanics
Hydrology/Water Resources
Industry
Iran
Machine tools
Mathematical models
mechanization
Mining
Orientation
Original Article
Parameters
Performance prediction
planning
prediction
Programming
Rocks
specific energy
Statistical analysis
tensile strength
Terrestrial Pollution
variance
Yields
Iran
Performance prediction
Gene expression programming (GEP)
Roadheader
Genetic programming (GP)
Instantaneous cutting rate (ICR)
ISSN:1866-6280, 1866-6299
Online Access:Get full text
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Summary:Roadheading machines play a vital role in excavation operation in tunneling and mining industries notably when selective mining is required. Roadheaders are more effective in soft to medium rock formations due to a higher cutting rate in such strata. A precise prediction of machine’s performance is a crucial issue, as it has considerable effects on excavation planning, project’s cost estimation, machine specification selection as well as safety of the project. In this research, a database of machine performance and some geomechanical parameters of rock formations from Tabas coal mine project, the largest and fully mechanized coal mine in Iran, has been established, including instantaneous cutting rate (ICR), uniaxial compressive strength, Brazilian tensile strength, rock quality designation, influence of discontinuity orientation (Alpha angle) and specific energy. Afterward, the parameters were analyzed through genetic programming (GP) and gene expression programming (GEP) approaches to yield more accurate models to predict the performance of roadheaders. As statistical indices, coefficient of determination, root mean square error and variance account were used to evaluate the efficiency of the developed models. According to the obtained results, it was observed that developed models can effectively be implemented for prediction of roadheader performance. Moreover, it was concluded that performance of the GEP model is better than the GP model. A high conformity was observed between predicted and measured roadheader ICR for GEP model.
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ISSN:1866-6280
1866-6299
DOI:10.1007/s12665-017-6920-2